ArticleNature communications2023
Teasing out missing reactions in genome-scale metabolic networks through hypergraph learning.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- MetabolEx: Platform for Small-Molecule Metabolites Generation and Metabolic Pathway Prediction.ACS measurement science au · 2026Article
- WASP: a pipeline for functional annotation prediction based on AlphaFold structural models.Nature communications · 2026Article
- Hypergraph learning with multi-dimensional metabolite feature extractions and static-dynamic attention mechanisms to fill missing reactions in metabolic networks.Briefings in bioinformatics · 2026Article
- Machine Learning Reveals Quantitative Amino Acid Preferences in Bifidobacterium longum Growth.Microbial biotechnology · 2026Article
- HNRM: hyperedge neighborhood-based representation for predicting N6-methyladenosine-related regulatory pathways.BMC biology · 2026Article
- A Novel Topology-Based Candidate Reaction Prediction Approach for Gap-Fillings of Genome-Scale Metabolic Models.Metabolites · 2026Article
- Tripleknock: predicting lethal effect of three-gene knockout in bacteria by deep learning.Scientific reports · 2026Article
- A multi-way SMILES-based hypergraph inference network for metabolic model reconstruction.Communications biology · 2026Article
- Automated biomedical hypothesis generation with time-aware hypergraph contrastive learning.Knowledge and information systems · 2026Article
- A fusion-based multiomics classification approach for enhanced gene discovery in non-small cell lung cancer.Bioinformatics advances · 2026Article
- Metabolic modeling unveils potential probiotic roles ofISME communications · 2026Article
- Genome-scale metabolic modelling of human gut microbes to inform rational community design.Gut microbes · 2025Article
- Computational approaches to enzymatic reaction assignment: a review of methods, validations, and future directions.Briefings in bioinformatics · 2025Review
- Hypergraph Semi-Supervised Contrastive Learning for Hyperedge Prediction Based on Enhanced Attention Aggregator.Entropy (Basel, Switzerland) · 2025Article
- Article
- Computational tools for nonnatural pathway design: Algorithms, applications, and challenges.Biodesign research · 2025Review
- Competition and cooperation: The plasticity of bacterial interactions across environments.PLoS computational biology · 2025Article
- Moving from genome-scale to community-scale metabolic models for the human gut microbiome.Nature microbiology · 2025Review
- A Guide to Metabolic Network Modeling for Plant Biology.Plants (Basel, Switzerland) · 2025Review
- Evolution and applications of genome-scale metabolic models in yeast systems biology studies.FEMS yeast research · 2025Review
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3 authors.
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Abstract
GEnome-scale Metabolic models (GEMs) are powerful tools to predict cellular metabolism and physiological states in living organisms. However, due to our imperfect knowledge of metabolic processes, even highly curated GEMs have knowledge gaps (e.g., missing reactions). Existing gap-filling methods typically require phenotypic data as input to tease out missing reactions. We still lack a computational method for rapid and accurate gap-filling of metabolic networks before experimental data is available. Here we present a deep learning-based method - CHEbyshev Spectral HyperlInk pREdictor (CHESHIRE) - to predict missing reactions in GEMs purely from metabolic network topology. We demonstrate that CHESHIRE outperforms other topology-based methods in predicting artificially removed reactions over 926 high- and intermediate-quality GEMs. Furthermore, CHESHIRE is able to improve the phenotypic predictions of 49 draft GEMs for fermentation products and amino acids secretions. Both types of validation suggest that CHESHIRE is a powerful tool for GEM curation to reveal unknown links between reactions and observed metabolic phenotypes.
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